The original U-Net [1]} architecture was trained for 30 epochs with Pan-Sharpened RGB+NIR 16-bit imagery, on a binary segmentation mask with a combination loss as described in the main text with \(\alpha =0.5\) . Dropout and batch normalization were used at each layer, with dropout with \(p=0.33\) . The same augmentation pipeline was used as with TernausNet. An Adam Optimizer  was used with learning rate of 0.0001 was used for training.
